| #!/bin/bash | |
| ## This gist contains instructions about cuda v11.2 and cudnn8.1 installation in Ubuntu 20.04 for Pytorch 1.8 & Tensorflow 2.7.0 | |
| ### steps #### | |
| # verify the system has a cuda-capable gpu | |
| # download and install the nvidia cuda toolkit and cudnn | |
| # setup environmental variables | |
| # verify the installation | |
| ### |
Building Tensorflow from source on Ubuntu 16.04LTS for maximum performance:
TensorFlow is now distributed under an Apache v2 open source license on GitHub.
On Ubuntu 16.04LTS+:
Step 1. Install NVIDIA CUDA:
To use TensorFlow with NVIDIA GPUs, the first step is to install the CUDA Toolkit as shown:
A list of commonly asked questions, design decisions, reasons why Clojure is the way it is as they were answered directly by Rich (even when from many years ago, those answers are pretty much valid today!). Feel free to point friends and colleagues here next time they ask (again). Answers are pasted verbatim (I've made small adjustments for readibility, but never changed a sentence) from mailing lists, articles, chats.
How to use:
| #http://blog.echen.me/2011/07/18/introduction-to-restricted-boltzmann-machines/ | |
| #https://www.tensorflow.org/versions/r0.7/api_docs/python/constant_op.html#random_uniform | |
| import tensorflow as tf | |
| import numpy as np | |
| import input_data | |
| import Image | |
| from util import tile_raster_images | |
| """ | |
| Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) | |
| BSD License | |
| """ | |
| import numpy as np | |
| # data I/O | |
| data = open('input.txt', 'r').read() # should be simple plain text file | |
| chars = list(set(data)) | |
| data_size, vocab_size = len(data), len(chars) |
| // adapted from intersectCube in https://github.com/evanw/webgl-path-tracing/blob/master/webgl-path-tracing.js | |
| // compute the near and far intersections of the cube (stored in the x and y components) using the slab method | |
| // no intersection means vec.x > vec.y (really tNear > tFar) | |
| vec2 intersectAABB(vec3 rayOrigin, vec3 rayDir, vec3 boxMin, vec3 boxMax) { | |
| vec3 tMin = (boxMin - rayOrigin) / rayDir; | |
| vec3 tMax = (boxMax - rayOrigin) / rayDir; | |
| vec3 t1 = min(tMin, tMax); | |
| vec3 t2 = max(tMin, tMax); | |
| float tNear = max(max(t1.x, t1.y), t1.z); |
(by @andrestaltz)
If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.
| *.so | |
| *.o | |
| *.html | |
| .*.un~ | |
| .*.swp |
Each of these commands will run an ad hoc http static server in your current (or specified) directory, available at http://localhost:8000. Use this power wisely.
$ python -m SimpleHTTPServer 8000